A computationally fast multimodal optimization with push enabled genetic algorithm

Dhebar, Yashesh; Deb, Kalyanmoy

doi:10.1145/3067695.3076023

Cited by 5 publications

(7 citation statements)

References 6 publications

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“…In this operation, two strings are selected randomly from the mating pool and some of their portions are exchanged. For instance, in a single point crossover operation, two new strings are produced by swapping the right-side portions of two strings after cutting these at arbitrary places, as presented in [15].…”

Section: Crossovermentioning

confidence: 99%

“…A better child string can be produced by combining good sub-strings from either parent if a suitable site is selected, which is usually selected randomly since the suitability of a site is not always known [15]. However, it must be noted that the random selection of a site does not make the search process random.…”

Section: Crossovermentioning

confidence: 99%

“…For a case of binary strings, the uniform crossover operation is applied by selecting from either parent every bit with a probability of 0.5 [15]. The major purpose of the crossover operation is the search of the parameter space and the preservation of the information from parent strings since these are labeled as good strings after due selection by the reproduction operation.…”

Section: Crossovermentioning

confidence: 99%

“…Maximum information is transferred or preserved from parent to child strings with a single-point crossover operator search as opposed to that of the uniform crossover operator, where the search is extensive but the information preservation form parent to child is minimal. For a crossover probability of P c , the crossover operation is applied to 100P c % of the strings in the population and the remaining strings, i.e., 100(1−P c )% are transferred to a new population [15].…”

Section: Crossovermentioning

confidence: 99%

“…The effectiveness of the proposed approach is demonstrated using five standard generating unit test systems (13, 40, 110, 140, and 160) and a 463 generating unit system formed by the combination of the five systems, and the results are compared with the best results presented in the literature. The choice of GA and AFSA is based on the following factors: (a) GA is a heuristic technique with a well-defined set of search equations that is effective in solving problems such optimal sizing and location of capacitor banks and distributed generators [15], optimal power flow [16], optimal location of tie and sectionalizing switches in distribution systems, and optimal network expansion [17], and (b) AFSA is a relatively new heuristic technique based on well-refined and sophisticated solution search equations and is widely applied in controller design, optimal PID tuning, and objective function minimization/maximization [18].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm

et al. 2021

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Economic Load Dispatch (ELD) plays a pivotal role in sustainable operation planning in a smart power system by reducing the fuel cost and by fulfilling the load demand in an efficient manner. In this work, the ELD problem is solved by using hybridized robust techniques that combine the Genetic Algorithm and Artificial Fish Swarm Algorithm, termed the Hybrid Genetic–Artificial Fish Swarm Algorithm (HGAFSA). The objective of this paper is threefold. First, the multi-objective ELD problem incorporating the effects of multiple fuels and valve-point loading and involving higher-order cost functions is optimally solved by HGAFSA. Secondly, the efficacy of HGAFSA is demonstrated using five standard generating unit test systems (13, 40, 110, 140, and 160). Finally, an extra-large system is formed by combining the five test systems, which result in a 463 generating unit system. The performance of the developed HGAFSA-based ELD algorithm is then tested on the six systems including the 463-unit system. Annual savings in fuel costs of $3.254 m, $0.38235 m, $2135.7, $9.5563 m, and $1.1588 m are achieved for the 13, 40, 110, 140, and 160 standard generating units, respectively, compared to costs mentioned in the available literature. The HGAFSA-based ELD optimization curves obtained during the optimization process are also presented.

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Section: Crossovermentioning

confidence: 99%

Section: Crossovermentioning

confidence: 99%

Section: Crossovermentioning

confidence: 99%

Section: Crossovermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm

et al. 2021

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Effect of a Push Operator in Genetic Algorithms for Multimodal Optimization

Dhebar

Deb

2017

Communications in Computer and Information Science

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Noncooperative Target Detection of Spacecraft Objects Based on Artificial Bee Colony Algorithm

Liu

Dong

et al. 2019

IEEE Intell. Syst.

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Accurate and fast recognition of Non-Cooperative Target (NCT) is an essential technology for Tethered Space Robots (TSR) when implementing on-orbit service. NCT is the spacecraft objects which could not be identified and located through the communication channels in the outer space. One of the approaches to resolve this problem is the use of intelligent computer vision system to track the object and hence to carry out operation and support tasks. By treating multi-object detection as the multi-peak optimization problem, this paper proposed a multi-circle detection method for recognition of circular modules on NCT and a multi-template matching method for other modules. Incorporating the concept of species into the Artificial Bee Colony (ABC) algorithm, a multi-peak optimization algorithm named as Species based Artificial Bee Colony (SABC) was proposed and applied to the above multi-object detection problem. In order to test and verify SABC, benchmark tests consisted of five multi-peak functions were conducted to verify the effectiveness for higher accuracy and shorter execution time in finding multiple optima. Firstly, we applied SABC into circle detection and design complete detection flow. Circular modules of NCT were adopted to the experiments and it was verified that the proposed multi-circle detection method based on SABC can locate circles with higher success rate and better accuracy than other methods. Furthermore, we employed SABC to multi-template matching and conducted experiments using simulated cases of China space missions as well as the Chang'e Camera Point System (CPS) developed by the Hong Kong Polytechnic University with China National Space Agency. Practical applications of multi-objects detection for NCT with kinds of noises, weakened brightness and shoot in continuous flights were evaluated in the experiments, and the results demonstrated that our methods were robust under different kinds of circumstances and thus confirmed its feasibility and effectiveness.

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A computationally fast multimodal optimization with push enabled genetic algorithm

Cited by 5 publications

References 6 publications

Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm

Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm

Effect of a Push Operator in Genetic Algorithms for Multimodal Optimization

Noncooperative Target Detection of Spacecraft Objects Based on Artificial Bee Colony Algorithm

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